docstr: ChannelAttention Forward.
  
  Args:
      x (torch.Tensor): input feature (batch, C, N, freq, time)
      ref_channel (None or int): index of the reference channel.
  Returns:
      output (torch.Tensor): output feature (batch, C, N, freq, time)
docstr_tok: ["ChannelAttention","Forward",".","Args",":","x","(","torch.Tensor",")",":","input","feature","(","batch",",","C",",","N",",","freq",",","time",")","ref_channel","(","None","or","int",")",":","index","of","the","reference","channel",".","Returns",":","output","(","torch.Tensor",")",":","output","feature","(","batch",",","C",",","N",",","freq",",","time",")"]
code: def forward(self, x, ref_channel=None):
      """ChannelAttention Forward.
  
      Args:
          x (torch.Tensor): input feature (batch, C, N, freq, time)
          ref_channel (None or int): index of the reference channel.
      Returns:
          output (torch.Tensor): output feature (batch, C, N, freq, time)
      """
      B, C, N, F, T = x.shape
      batch = x.permute(0, 4, 1, 3, 2)  # [B, T, C, F, N]
  
      Q = (
          self.attn_conv_Q(batch)
          .reshape(B, T, C, F, -1, self.att_heads)
          .permute(0, 5, 1, 2, 3, 4)
          .contiguous()
      )  # [B, head, T, C, F, D]
      K = (
          self.attn_conv_K(batch)
          .reshape(B, T, C, F, -1, self.att_heads)
          .permute(0, 5, 1, 2, 3, 4)
          .contiguous()
      )  # [B, head, T, C, F, D]
      V = (
          self.attn_conv_V(batch)
          .reshape(B, T, C, F, -1, self.att_heads)
          .permute(0, 5, 1, 2, 3, 4)
          .contiguous()
      )  # [B, head, T, C, F, D']
  
      emb_dim = V.size(-2) * V.size(-1)
      attn_mat = torch.einsum(
          "bhtcfn,bhtefn->bhce", Q / T, K / emb_dim**0.5
      )
      attn_mat = nn.functional.softmax(
          attn_mat, dim=-1
      )  # [B, head, C, C]
      V = torch.einsum(
          "bhce,bhtefn->bhtcfn", attn_mat, V
      )  # [B, head, T, C, F, D']
  
      batch = torch.cat(V.unbind(dim=1), dim=-1)  # [B, T, C, F, D]
      batch = self["attn_concat_proj"](batch)  # [B, T, C, F, N]
  
      return batch.permute(0, 2, 4, 3, 1) + x
  
imports: import warnings
  import torch
  import torch.nn
  from espnet2.enh.layers.dptnet import ImprovedTransformerLayer
  from espnet2.enh.layers.tcn import ChannelwiseLayerNorm
  from espnet2.torch_utils.get_layer_from_string import get_layer
label_desc: Use image node_id 3 for calling the ChannelAttention obj's underlying member method code with example usage: obj.forward(x, ref_channel) and returns: unknown